Overview

Dataset statistics

Number of variables19
Number of observations18
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory159.1 B

Variable types

Categorical2
Numeric17

Alerts

Age is highly overall correlated with PlayerHigh correlation
Tkl is highly overall correlated with TklW and 11 other fieldsHigh correlation
TklW is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Def 3rd is highly overall correlated with Tkl and 10 other fieldsHigh correlation
Mid 3rd is highly overall correlated with Tkl and 7 other fieldsHigh correlation
Att 3rd is highly overall correlated with Sh and 1 other fieldsHigh correlation
Tkl.1 is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Att is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Tkl% is highly overall correlated with PlayerHigh correlation
Lost is highly overall correlated with Tkl and 10 other fieldsHigh correlation
Blocks is highly overall correlated with Tkl and 10 other fieldsHigh correlation
Sh is highly overall correlated with Att 3rd and 1 other fieldsHigh correlation
Pass is highly overall correlated with Tkl and 9 other fieldsHigh correlation
Int is highly overall correlated with Tkl and 12 other fieldsHigh correlation
Tkl+Int is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Clr is highly overall correlated with Tkl and 11 other fieldsHigh correlation
Err is highly overall correlated with Int and 1 other fieldsHigh correlation
Player is highly overall correlated with Age and 17 other fieldsHigh correlation
90s is highly overall correlated with Clr and 1 other fieldsHigh correlation
Player is uniformly distributedUniform
Player has unique valuesUnique
Tkl% has unique valuesUnique
Err has 2 (11.1%) zerosZeros

Reproduction

Analysis started2023-02-11 17:32:55.064803
Analysis finished2023-02-11 17:33:21.726673
Duration26.66 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Player
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size272.0 B
England
 
1
EnglandOpp
 
1
Average
 
1
USAOpp
 
1
USA
 
1
Other values (13)
13 

Length

Max length14
Median length10
Mean length7.9444444
Min length3

Characters and Unicode

Total characters143
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)100.0%

Sample

1st rowEngland
2nd rowEnglandOpp
3rd rowFrance
4th rowFranceOpp
5th rowGermany

Common Values

ValueCountFrequency (%)
England 1
 
5.6%
EnglandOpp 1
 
5.6%
Average 1
 
5.6%
USAOpp 1
 
5.6%
USA 1
 
5.6%
SwedenOpp 1
 
5.6%
Sweden 1
 
5.6%
NorwayOpp 1
 
5.6%
Norway 1
 
5.6%
NetherlandsOpp 1
 
5.6%
Other values (8) 8
44.4%

Length

2023-02-11T12:33:21.796688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
england 1
 
5.6%
englandopp 1
 
5.6%
france 1
 
5.6%
franceopp 1
 
5.6%
germany 1
 
5.6%
germanyopp 1
 
5.6%
italy 1
 
5.6%
italyopp 1
 
5.6%
netherlands 1
 
5.6%
netherlandsopp 1
 
5.6%
Other values (8) 8
44.4%

Most occurring characters

ValueCountFrequency (%)
p 18
12.6%
e 16
 
11.2%
a 14
 
9.8%
n 12
 
8.4%
r 10
 
7.0%
O 9
 
6.3%
l 6
 
4.2%
d 6
 
4.2%
y 6
 
4.2%
t 4
 
2.8%
Other values (16) 42
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112
78.3%
Uppercase Letter 31
 
21.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 18
16.1%
e 16
14.3%
a 14
12.5%
n 12
10.7%
r 10
8.9%
l 6
 
5.4%
d 6
 
5.4%
y 6
 
5.4%
t 4
 
3.6%
w 4
 
3.6%
Other values (7) 16
14.3%
Uppercase Letter
ValueCountFrequency (%)
O 9
29.0%
N 4
12.9%
S 4
12.9%
A 4
12.9%
F 2
 
6.5%
G 2
 
6.5%
I 2
 
6.5%
E 2
 
6.5%
U 2
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 143
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 18
12.6%
e 16
 
11.2%
a 14
 
9.8%
n 12
 
8.4%
r 10
 
7.0%
O 9
 
6.3%
l 6
 
4.2%
d 6
 
4.2%
y 6
 
4.2%
t 4
 
2.8%
Other values (16) 42
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 18
12.6%
e 16
 
11.2%
a 14
 
9.8%
n 12
 
8.4%
r 10
 
7.0%
O 9
 
6.3%
l 6
 
4.2%
d 6
 
4.2%
y 6
 
4.2%
t 4
 
2.8%
Other values (16) 42
29.4%

Age
Real number (ℝ)

Distinct15
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.09375
Minimum25.5
Maximum28.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:21.881707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25.5
5-th percentile25.84
Q126.725
median26.98125
Q327.65625
95-th percentile28.545
Maximum28.8
Range3.3
Interquartile range (IQR)0.93125

Descriptive statistics

Standard deviation0.8479674
Coefficient of variation (CV)0.031297528
Kurtosis0.10399513
Mean27.09375
Median Absolute Deviation (MAD)0.38125
Skewness0.21029072
Sum487.6875
Variance0.71904871
MonotonicityNot monotonic
2023-02-11T12:33:21.975729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
27.8 2
 
11.1%
26.9 2
 
11.1%
27.2 2
 
11.1%
27.9 1
 
5.6%
25.5 1
 
5.6%
26.7 1
 
5.6%
26.5 1
 
5.6%
25.9 1
 
5.6%
26.1 1
 
5.6%
26.8 1
 
5.6%
Other values (5) 5
27.8%
ValueCountFrequency (%)
25.5 1
5.6%
25.9 1
5.6%
26.1 1
5.6%
26.5 1
5.6%
26.7 1
5.6%
26.8 1
5.6%
26.9 2
11.1%
26.9625 1
5.6%
27 1
5.6%
27.2 2
11.1%
ValueCountFrequency (%)
28.8 1
5.6%
28.5 1
5.6%
27.9 1
5.6%
27.8 2
11.1%
27.225 1
5.6%
27.2 2
11.1%
27 1
5.6%
26.9625 1
5.6%
26.9 2
11.1%
26.8 1
5.6%

90s
Categorical

Distinct5
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size272.0 B
7.0
5.3
5.0
7.3
6.15

Length

Max length4
Median length3
Mean length3.1111111
Min length3

Characters and Unicode

Total characters56
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7.0
2nd row7.0
3rd row5.3
4th row5.3
5th row5.0

Common Values

ValueCountFrequency (%)
7.0 4
22.2%
5.3 4
22.2%
5.0 4
22.2%
7.3 4
22.2%
6.15 2
11.1%

Length

2023-02-11T12:33:22.086754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-11T12:33:22.197778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
7.0 4
22.2%
5.3 4
22.2%
5.0 4
22.2%
7.3 4
22.2%
6.15 2
11.1%

Most occurring characters

ValueCountFrequency (%)
. 18
32.1%
5 10
17.9%
7 8
14.3%
0 8
14.3%
3 8
14.3%
6 2
 
3.6%
1 2
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38
67.9%
Other Punctuation 18
32.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10
26.3%
7 8
21.1%
0 8
21.1%
3 8
21.1%
6 2
 
5.3%
1 2
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 18
32.1%
5 10
17.9%
7 8
14.3%
0 8
14.3%
3 8
14.3%
6 2
 
3.6%
1 2
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 18
32.1%
5 10
17.9%
7 8
14.3%
0 8
14.3%
3 8
14.3%
6 2
 
3.6%
1 2
 
3.6%

Tkl
Real number (ℝ)

Distinct17
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.5625
Minimum81
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:22.294801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile86.1
Q1108.25
median120.875
Q3134.09375
95-th percentile179.45
Maximum182
Range101
Interquartile range (IQR)25.84375

Descriptive statistics

Standard deviation30.226846
Coefficient of variation (CV)0.2388294
Kurtosis-0.29176503
Mean126.5625
Median Absolute Deviation (MAD)14.5
Skewness0.61522635
Sum2278.125
Variance913.66222
MonotonicityNot monotonic
2023-02-11T12:33:22.391822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
115 2
 
11.1%
135 1
 
5.6%
87 1
 
5.6%
121.75 1
 
5.6%
179 1
 
5.6%
117 1
 
5.6%
178 1
 
5.6%
131 1
 
5.6%
95 1
 
5.6%
155 1
 
5.6%
Other values (7) 7
38.9%
ValueCountFrequency (%)
81 1
5.6%
87 1
5.6%
95 1
5.6%
101 1
5.6%
106 1
5.6%
115 2
11.1%
117 1
5.6%
120 1
5.6%
121.75 1
5.6%
128 1
5.6%
ValueCountFrequency (%)
182 1
5.6%
179 1
5.6%
178 1
5.6%
155 1
5.6%
135 1
5.6%
131.375 1
5.6%
131 1
5.6%
128 1
5.6%
121.75 1
5.6%
120 1
5.6%

TklW
Real number (ℝ)

Distinct17
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.4375
Minimum45
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:22.485844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile49.25
Q171
median73.875
Q382.53125
95-th percentile112.3
Maximum114
Range69
Interquartile range (IQR)11.53125

Descriptive statistics

Standard deviation18.835406
Coefficient of variation (CV)0.24323365
Kurtosis0.067774212
Mean77.4375
Median Absolute Deviation (MAD)8.1875
Skewness0.46505256
Sum1393.875
Variance354.77252
MonotonicityNot monotonic
2023-02-11T12:33:22.570863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
71 2
 
11.1%
60 1
 
5.6%
73.75 1
 
5.6%
102 1
 
5.6%
74 1
 
5.6%
112 1
 
5.6%
78 1
 
5.6%
62 1
 
5.6%
96 1
 
5.6%
76 1
 
5.6%
Other values (7) 7
38.9%
ValueCountFrequency (%)
45 1
5.6%
50 1
5.6%
60 1
5.6%
62 1
5.6%
71 2
11.1%
72 1
5.6%
73 1
5.6%
73.75 1
5.6%
74 1
5.6%
76 1
5.6%
ValueCountFrequency (%)
114 1
5.6%
112 1
5.6%
102 1
5.6%
96 1
5.6%
83 1
5.6%
81.125 1
5.6%
78 1
5.6%
76 1
5.6%
74 1
5.6%
73.75 1
5.6%

Def 3rd
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.9375
Minimum36
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:22.659883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile39.4
Q149
median55.6875
Q381.125
95-th percentile97.5
Maximum106
Range70
Interquartile range (IQR)32.125

Descriptive statistics

Standard deviation21.223697
Coefficient of variation (CV)0.33194443
Kurtosis-0.82043294
Mean63.9375
Median Absolute Deviation (MAD)12
Skewness0.6662098
Sum1150.875
Variance450.44531
MonotonicityNot monotonic
2023-02-11T12:33:22.743903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
91 2
 
11.1%
49 2
 
11.1%
55 1
 
5.6%
61 1
 
5.6%
40 1
 
5.6%
84 1
 
5.6%
44 1
 
5.6%
56 1
 
5.6%
68 1
 
5.6%
36 1
 
5.6%
Other values (6) 6
33.3%
ValueCountFrequency (%)
36 1
5.6%
40 1
5.6%
44 1
5.6%
47 1
5.6%
49 2
11.1%
50 1
5.6%
55 1
5.6%
55.375 1
5.6%
56 1
5.6%
61 1
5.6%
ValueCountFrequency (%)
106 1
5.6%
96 1
5.6%
91 2
11.1%
84 1
5.6%
72.5 1
5.6%
68 1
5.6%
61 1
5.6%
56 1
5.6%
55.375 1
5.6%
55 1
5.6%

Mid 3rd
Real number (ℝ)

Distinct17
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.3125
Minimum27
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:22.831932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile28.7
Q140.5
median51
Q360
95-th percentile69.2
Maximum76
Range49
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation13.564677
Coefficient of variation (CV)0.26960849
Kurtosis-0.49029893
Mean50.3125
Median Absolute Deviation (MAD)11
Skewness0.0063677098
Sum905.625
Variance184.00046
MonotonicityNot monotonic
2023-02-11T12:33:22.931980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
62 2
 
11.1%
54 1
 
5.6%
52.125 1
 
5.6%
52 1
 
5.6%
67 1
 
5.6%
68 1
 
5.6%
39 1
 
5.6%
27 1
 
5.6%
76 1
 
5.6%
50 1
 
5.6%
Other values (7) 7
38.9%
ValueCountFrequency (%)
27 1
5.6%
29 1
5.6%
34 1
5.6%
38 1
5.6%
39 1
5.6%
45 1
5.6%
48.5 1
5.6%
49 1
5.6%
50 1
5.6%
52 1
5.6%
ValueCountFrequency (%)
76 1
5.6%
68 1
5.6%
67 1
5.6%
62 2
11.1%
54 1
5.6%
53 1
5.6%
52.125 1
5.6%
52 1
5.6%
50 1
5.6%
49 1
5.6%

Att 3rd
Real number (ℝ)

Distinct12
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3125
Minimum6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:23.262134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q110.09375
median11.5
Q315.75
95-th percentile17.15
Maximum18
Range12
Interquartile range (IQR)5.65625

Descriptive statistics

Standard deviation3.872568
Coefficient of variation (CV)0.31452329
Kurtosis-1.1360828
Mean12.3125
Median Absolute Deviation (MAD)3.5
Skewness-0.20449202
Sum221.625
Variance14.996783
MonotonicityNot monotonic
2023-02-11T12:33:23.348422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 3
16.7%
17 2
11.1%
6 2
11.1%
15 2
11.1%
16 2
11.1%
18 1
 
5.6%
7 1
 
5.6%
12 1
 
5.6%
9 1
 
5.6%
10 1
 
5.6%
Other values (2) 2
11.1%
ValueCountFrequency (%)
6 2
11.1%
7 1
 
5.6%
9 1
 
5.6%
10 1
 
5.6%
10.375 1
 
5.6%
11 3
16.7%
12 1
 
5.6%
14.25 1
 
5.6%
15 2
11.1%
16 2
11.1%
ValueCountFrequency (%)
18 1
 
5.6%
17 2
11.1%
16 2
11.1%
15 2
11.1%
14.25 1
 
5.6%
12 1
 
5.6%
11 3
16.7%
10.375 1
 
5.6%
10 1
 
5.6%
9 1
 
5.6%

Tkl.1
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.9375
Minimum25
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:23.443949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile26.7
Q139.5
median44
Q351.40625
95-th percentile73.1
Maximum85
Range60
Interquartile range (IQR)11.90625

Descriptive statistics

Standard deviation15.65754
Coefficient of variation (CV)0.33358274
Kurtosis0.85911596
Mean46.9375
Median Absolute Deviation (MAD)6.9375
Skewness0.91880212
Sum844.875
Variance245.15855
MonotonicityNot monotonic
2023-02-11T12:33:23.523990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
42 2
 
11.1%
44 2
 
11.1%
39 1
 
5.6%
27 1
 
5.6%
46 1
 
5.6%
25 1
 
5.6%
49 1
 
5.6%
29 1
 
5.6%
71 1
 
5.6%
69 1
 
5.6%
Other values (6) 6
33.3%
ValueCountFrequency (%)
25 1
5.6%
27 1
5.6%
29 1
5.6%
34 1
5.6%
39 1
5.6%
41 1
5.6%
42 2
11.1%
44 2
11.1%
46 1
5.6%
49 1
5.6%
ValueCountFrequency (%)
85 1
5.6%
71 1
5.6%
69 1
5.6%
56 1
5.6%
51.875 1
5.6%
50 1
5.6%
49 1
5.6%
46 1
5.6%
44 2
11.1%
42 2
11.1%

Att
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.1875
Minimum52
Maximum149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:23.615566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile53.7
Q182.71875
median87
Q3113.375
95-th percentile148.15
Maximum149
Range97
Interquartile range (IQR)30.65625

Descriptive statistics

Standard deviation28.739328
Coefficient of variation (CV)0.30192334
Kurtosis-0.3768056
Mean95.1875
Median Absolute Deviation (MAD)17.25
Skewness0.50431151
Sum1713.375
Variance825.94899
MonotonicityNot monotonic
2023-02-11T12:33:23.699583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
87 3
16.7%
116 1
 
5.6%
52 1
 
5.6%
94 1
 
5.6%
54 1
 
5.6%
85 1
 
5.6%
92 1
 
5.6%
64 1
 
5.6%
149 1
 
5.6%
125 1
 
5.6%
Other values (6) 6
33.3%
ValueCountFrequency (%)
52 1
 
5.6%
54 1
 
5.6%
64 1
 
5.6%
71 1
 
5.6%
82 1
 
5.6%
84.875 1
 
5.6%
85 1
 
5.6%
87 3
16.7%
92 1
 
5.6%
94 1
 
5.6%
ValueCountFrequency (%)
149 1
 
5.6%
148 1
 
5.6%
130 1
 
5.6%
125 1
 
5.6%
116 1
 
5.6%
105.5 1
 
5.6%
94 1
 
5.6%
92 1
 
5.6%
87 3
16.7%
85 1
 
5.6%

Tkl%
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct18
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.25625
Minimum36.2
Maximum57.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:23.787602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum36.2
5-th percentile42.065
Q146.5
median48.925
Q352.95
95-th percentile57.415
Maximum57.5
Range21.3
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation5.31926
Coefficient of variation (CV)0.10799157
Kurtosis0.81169837
Mean49.25625
Median Absolute Deviation (MAD)3.3
Skewness-0.50670889
Sum886.6125
Variance28.294527
MonotonicityNot monotonic
2023-02-11T12:33:23.869622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44.8 1
 
5.6%
36.2 1
 
5.6%
49.5625 1
 
5.6%
43.1 1
 
5.6%
47.1 1
 
5.6%
57.4 1
 
5.6%
57.5 1
 
5.6%
53.7 1
 
5.6%
47.9 1
 
5.6%
55.2 1
 
5.6%
Other values (8) 8
44.4%
ValueCountFrequency (%)
36.2 1
5.6%
43.1 1
5.6%
44.8 1
5.6%
45.3 1
5.6%
46.3 1
5.6%
47.1 1
5.6%
47.7 1
5.6%
47.9 1
5.6%
48.9 1
5.6%
48.95 1
5.6%
ValueCountFrequency (%)
57.5 1
5.6%
57.4 1
5.6%
55.2 1
5.6%
53.7 1
5.6%
53.3 1
5.6%
51.9 1
5.6%
51.8 1
5.6%
49.5625 1
5.6%
48.95 1
5.6%
48.9 1
5.6%

Lost
Real number (ℝ)

Distinct15
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.25
Minimum25
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:23.953651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile28.4
Q137.25
median44.5
Q355.40625
95-th percentile74.6
Maximum78
Range53
Interquartile range (IQR)18.15625

Descriptive statistics

Standard deviation15.485821
Coefficient of variation (CV)0.32094966
Kurtosis-0.37346414
Mean48.25
Median Absolute Deviation (MAD)8.3125
Skewness0.67115302
Sum868.5
Variance239.81066
MonotonicityNot monotonic
2023-02-11T12:33:24.043660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
48 2
 
11.1%
74 2
 
11.1%
37 2
 
11.1%
25 1
 
5.6%
29 1
 
5.6%
41 1
 
5.6%
43 1
 
5.6%
35 1
 
5.6%
78 1
 
5.6%
56 1
 
5.6%
Other values (5) 5
27.8%
ValueCountFrequency (%)
25 1
5.6%
29 1
5.6%
35 1
5.6%
37 2
11.1%
38 1
5.6%
41 1
5.6%
42.875 1
5.6%
43 1
5.6%
46 1
5.6%
48 2
11.1%
ValueCountFrequency (%)
78 1
5.6%
74 2
11.1%
63 1
5.6%
56 1
5.6%
53.625 1
5.6%
48 2
11.1%
46 1
5.6%
43 1
5.6%
42.875 1
5.6%
41 1
5.6%

Blocks
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.1875
Minimum44
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:24.151685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile51.65
Q167.96875
median74.375
Q377.75
95-th percentile84.15
Maximum85
Range41
Interquartile range (IQR)9.78125

Descriptive statistics

Standard deviation11.339209
Coefficient of variation (CV)0.15928652
Kurtosis0.52398486
Mean71.1875
Median Absolute Deviation (MAD)6
Skewness-1.0264539
Sum1281.375
Variance128.57767
MonotonicityNot monotonic
2023-02-11T12:33:24.231703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
71 2
 
11.1%
77 2
 
11.1%
74 1
 
5.6%
84 1
 
5.6%
44 1
 
5.6%
81 1
 
5.6%
57 1
 
5.6%
53 1
 
5.6%
85 1
 
5.6%
78 1
 
5.6%
Other values (6) 6
33.3%
ValueCountFrequency (%)
44 1
5.6%
53 1
5.6%
57 1
5.6%
59 1
5.6%
67.625 1
5.6%
69 1
5.6%
71 2
11.1%
74 1
5.6%
74.75 1
5.6%
76 1
5.6%
ValueCountFrequency (%)
85 1
5.6%
84 1
5.6%
83 1
5.6%
81 1
5.6%
78 1
5.6%
77 2
11.1%
76 1
5.6%
74.75 1
5.6%
74 1
5.6%
71 2
11.1%

Sh
Real number (ℝ)

Distinct13
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.125
Minimum7
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:24.313722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8.7
Q114.25
median17.5
Q320.75
95-th percentile24.15
Maximum25
Range18
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation5.1119813
Coefficient of variation (CV)0.29850986
Kurtosis-0.35967469
Mean17.125
Median Absolute Deviation (MAD)3.5
Skewness-0.36010811
Sum308.25
Variance26.132353
MonotonicityNot monotonic
2023-02-11T12:33:24.396740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
21 2
11.1%
24 2
11.1%
18 2
11.1%
17 2
11.1%
14 2
11.1%
9 1
 
5.6%
7 1
 
5.6%
10 1
 
5.6%
15 1
 
5.6%
25 1
 
5.6%
Other values (3) 3
16.7%
ValueCountFrequency (%)
7 1
5.6%
9 1
5.6%
10 1
5.6%
14 2
11.1%
15 1
5.6%
15.625 1
5.6%
17 2
11.1%
18 2
11.1%
18.625 1
5.6%
20 1
5.6%
ValueCountFrequency (%)
25 1
5.6%
24 2
11.1%
21 2
11.1%
20 1
5.6%
18.625 1
5.6%
18 2
11.1%
17 2
11.1%
15.625 1
5.6%
15 1
5.6%
14 2
11.1%

Pass
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.0625
Minimum37
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:24.486771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile38.7
Q146.5
median55
Q359
95-th percentile69.15
Maximum70
Range33
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation9.5838262
Coefficient of variation (CV)0.17727309
Kurtosis-0.62082107
Mean54.0625
Median Absolute Deviation (MAD)6
Skewness-0.11879367
Sum973.125
Variance91.849724
MonotonicityNot monotonic
2023-02-11T12:33:24.571790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
45 2
 
11.1%
59 2
 
11.1%
65 1
 
5.6%
63 1
 
5.6%
37 1
 
5.6%
57 1
 
5.6%
39 1
 
5.6%
54 1
 
5.6%
56 1
 
5.6%
43 1
 
5.6%
Other values (6) 6
33.3%
ValueCountFrequency (%)
37 1
5.6%
39 1
5.6%
43 1
5.6%
45 2
11.1%
51 1
5.6%
52 1
5.6%
53 1
5.6%
54 1
5.6%
56 1
5.6%
56.125 1
5.6%
ValueCountFrequency (%)
70 1
5.6%
69 1
5.6%
65 1
5.6%
63 1
5.6%
59 2
11.1%
57 1
5.6%
56.125 1
5.6%
56 1
5.6%
54 1
5.6%
53 1
5.6%

Int
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.9375
Minimum44
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:24.664800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile49.1
Q157.5
median68
Q383.5
95-th percentile95.45
Maximum98
Range54
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.599978
Coefficient of variation (CV)0.23735447
Kurtosis-1.0212224
Mean69.9375
Median Absolute Deviation (MAD)14.5
Skewness0.17890981
Sum1258.875
Variance275.55928
MonotonicityNot monotonic
2023-02-11T12:33:24.756334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
50 3
16.7%
70 1
 
5.6%
92 1
 
5.6%
56 1
 
5.6%
69 1
 
5.6%
62 1
 
5.6%
44 1
 
5.6%
98 1
 
5.6%
85 1
 
5.6%
67 1
 
5.6%
Other values (6) 6
33.3%
ValueCountFrequency (%)
44 1
 
5.6%
50 3
16.7%
56 1
 
5.6%
62 1
 
5.6%
64.125 1
 
5.6%
66 1
 
5.6%
67 1
 
5.6%
69 1
 
5.6%
70 1
 
5.6%
75.75 1
 
5.6%
ValueCountFrequency (%)
98 1
5.6%
95 1
5.6%
92 1
5.6%
86 1
5.6%
85 1
5.6%
79 1
5.6%
75.75 1
5.6%
70 1
5.6%
69 1
5.6%
67 1
5.6%

Tkl+Int
Real number (ℝ)

Distinct17
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.5
Minimum131
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:24.856387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum131
5-th percentile136.1
Q1162
median192.5
Q3216.78125
95-th percentile274.05
Maximum280
Range149
Interquartile range (IQR)54.78125

Descriptive statistics

Standard deviation44.973052
Coefficient of variation (CV)0.22887049
Kurtosis-0.5598212
Mean196.5
Median Absolute Deviation (MAD)30.5
Skewness0.52283635
Sum3537
Variance2022.5754
MonotonicityNot monotonic
2023-02-11T12:33:24.949394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
162 2
 
11.1%
205 1
 
5.6%
240 1
 
5.6%
185.875 1
 
5.6%
265 1
 
5.6%
196 1
 
5.6%
273 1
 
5.6%
197 1
 
5.6%
137 1
 
5.6%
280 1
 
5.6%
Other values (7) 7
38.9%
ValueCountFrequency (%)
131 1
5.6%
137 1
5.6%
151 1
5.6%
159 1
5.6%
162 2
11.1%
177 1
5.6%
185.875 1
5.6%
189 1
5.6%
196 1
5.6%
197 1
5.6%
ValueCountFrequency (%)
280 1
5.6%
273 1
5.6%
265 1
5.6%
240 1
5.6%
220 1
5.6%
207.125 1
5.6%
205 1
5.6%
197 1
5.6%
196 1
5.6%
189 1
5.6%

Clr
Real number (ℝ)

Distinct16
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.75
Minimum53
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:25.053418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile81.05
Q198
median122
Q3146.5
95-th percentile160.2
Maximum184
Range131
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation32.40325
Coefficient of variation (CV)0.26614579
Kurtosis-0.17476346
Mean121.75
Median Absolute Deviation (MAD)24.5
Skewness-0.17261824
Sum2191.5
Variance1049.9706
MonotonicityNot monotonic
2023-02-11T12:33:25.144438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
98 2
 
11.1%
122 2
 
11.1%
148 1
 
5.6%
53 1
 
5.6%
143 1
 
5.6%
92 1
 
5.6%
90 1
 
5.6%
147 1
 
5.6%
156 1
 
5.6%
111 1
 
5.6%
Other values (6) 6
33.3%
ValueCountFrequency (%)
53 1
5.6%
86 1
5.6%
90 1
5.6%
92 1
5.6%
98 2
11.1%
108.25 1
5.6%
111 1
5.6%
122 2
11.1%
135.25 1
5.6%
143 1
5.6%
ValueCountFrequency (%)
184 1
5.6%
156 1
5.6%
153 1
5.6%
148 1
5.6%
147 1
5.6%
145 1
5.6%
143 1
5.6%
135.25 1
5.6%
122 2
11.1%
111 1
5.6%

Err
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0625
Minimum0
Maximum8
Zeros2
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-02-11T12:33:25.232469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1.9375
Q32.8125
95-th percentile4.6
Maximum8
Range8
Interquartile range (IQR)1.8125

Descriptive statistics

Standard deviation1.8312293
Coefficient of variation (CV)0.88786875
Kurtosis6.1008418
Mean2.0625
Median Absolute Deviation (MAD)0.9375
Skewness2.0940817
Sum37.125
Variance3.3534007
MonotonicityNot monotonic
2023-02-11T12:33:25.320478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 6
33.3%
3 3
16.7%
2 3
16.7%
0 2
 
11.1%
8 1
 
5.6%
4 1
 
5.6%
1.875 1
 
5.6%
2.25 1
 
5.6%
ValueCountFrequency (%)
0 2
 
11.1%
1 6
33.3%
1.875 1
 
5.6%
2 3
16.7%
2.25 1
 
5.6%
3 3
16.7%
4 1
 
5.6%
8 1
 
5.6%
ValueCountFrequency (%)
8 1
 
5.6%
4 1
 
5.6%
3 3
16.7%
2.25 1
 
5.6%
2 3
16.7%
1.875 1
 
5.6%
1 6
33.3%
0 2
 
11.1%

Interactions

2023-02-11T12:33:19.839178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:55.550889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:56.993215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:58.692598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:00.153928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:01.619264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:03.214743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:04.623949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:06.277322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:07.620643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:08.947681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:10.622329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:11.954662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:13.476863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:15.197697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:16.601053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:18.118299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:19.927198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:55.641909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:57.082235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:58.779618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:00.243949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:01.944873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:03.299762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:04.708969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:06.359342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:07.699664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:09.035703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:10.708351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:12.044682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:13.568764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:15.291719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:16.698075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:18.206320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:20.011217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:55.723928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-02-11T12:33:16.259971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:17.761214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:19.467095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:21.061522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:56.728154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:58.432539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:59.883867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:01.355204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:02.968682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:04.364891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:05.759205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:07.375576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:08.702752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:10.355260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:11.710595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:13.195798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:14.917735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:16.341995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:17.849233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:19.551113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:21.151542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:56.816174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:58.523560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:59.979889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:01.447224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:03.052703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:04.452911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:06.102283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:07.457598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:08.784770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:10.440279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:11.793626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:13.287818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:15.005755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:16.430014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:17.940259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:19.653138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:21.240562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:56.904194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:32:58.606579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:00.065908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:01.531243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:03.131721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:04.535929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:06.188303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:07.536620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:08.863657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:10.523301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:11.871643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:13.377839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:15.098778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:16.515033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:18.027279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-11T12:33:19.747158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-11T12:33:25.427513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
AgeTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntTkl+IntClrErrPlayer90s
Age1.0000.3140.3340.0350.2900.3730.1070.1200.2400.0940.001-0.1700.1030.3010.3260.0920.2981.0000.000
Tkl0.3141.0000.8830.7710.8060.2840.7650.8720.0090.8400.5860.0220.6010.8300.9610.7790.2631.0000.080
TklW0.3340.8831.0000.8360.6310.0480.8740.9090.1740.8240.6700.1990.5390.8200.9040.8550.1911.0000.352
Def 3rd0.0350.7710.8361.0000.394-0.2610.8510.9040.0700.8850.7180.4180.5410.6840.7620.8300.2731.0000.147
Mid 3rd0.2900.8060.6310.3941.0000.4500.5440.5480.1380.4920.315-0.3520.4780.6470.7790.5070.1961.0000.293
Att 3rd0.3730.2840.048-0.2610.4501.000-0.2550.009-0.3010.066-0.122-0.5670.0970.2810.290-0.0560.1171.0000.378
Tkl.10.1070.7650.8740.8510.544-0.2551.0000.8750.4130.7320.7510.2940.6280.6470.7610.8150.0761.0000.436
Att0.1200.8720.9090.9040.5480.0090.8751.0000.0070.9500.7970.2910.6670.8260.8910.8870.2611.0000.000
Tkl%0.2400.0090.1740.0700.138-0.3010.4130.0071.000-0.2110.2170.0540.165-0.125-0.0030.068-0.1371.0000.235
Lost0.0940.8400.8240.8850.4920.0660.7320.950-0.2111.0000.7350.2550.6370.8700.8790.7900.4131.0000.000
Blocks0.0010.5860.6700.7180.315-0.1220.7510.7970.2170.7351.0000.4090.8690.6290.6480.6890.2331.0000.000
Sh-0.1700.0220.1990.418-0.352-0.5670.2940.2910.0540.2550.4091.0000.0290.0010.0250.492-0.1611.0000.000
Pass0.1030.6010.5390.5410.4780.0970.6280.6670.1650.6370.8690.0291.0000.6070.6360.4800.3801.0000.196
Int0.3010.8300.8200.6840.6470.2810.6470.826-0.1250.8700.6290.0010.6071.0000.9430.6600.5111.0000.313
Tkl+Int0.3260.9610.9040.7620.7790.2900.7610.891-0.0030.8790.6480.0250.6360.9431.0000.7760.3881.0000.401
Clr0.0920.7790.8550.8300.507-0.0560.8150.8870.0680.7900.6890.4920.4800.6600.7761.0000.0871.0000.533
Err0.2980.2630.1910.2730.1960.1170.0760.261-0.1370.4130.233-0.1610.3800.5110.3880.0871.0001.0000.136
Player1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
90s0.0000.0800.3520.1470.2930.3780.4360.0000.2350.0000.0000.0000.1960.3130.4010.5330.1361.0001.000

Missing values

2023-02-11T12:33:21.396598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-11T12:33:21.629652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PlayerAge90sTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntTkl+IntClrErr
0England27.87.0135.071.055.062.018.039.087.044.848.074.09.065.070.0205.098.08.0
1EnglandOpp26.97.0128.076.061.050.017.042.0116.036.274.084.021.063.092.0220.0148.03.0
2France27.85.3106.071.040.049.017.027.052.051.925.044.07.037.056.0162.053.01.0
3FranceOpp27.95.3120.083.084.029.07.046.094.048.948.081.024.057.069.0189.0143.02.0
4Germany25.55.0101.050.044.045.012.025.054.046.329.057.018.039.050.0151.092.01.0
5GermanyOpp26.75.0115.073.056.053.06.044.085.051.841.071.017.054.062.0177.0122.03.0
6Italy26.55.0115.072.068.038.09.049.092.053.343.077.021.056.044.0159.0122.00.0
7ItalyOpp25.95.081.045.036.034.011.029.064.045.335.053.010.043.050.0131.090.01.0
8Netherlands26.17.3182.0114.091.076.015.071.0149.047.778.085.015.070.098.0280.0147.01.0
9NetherlandsOpp27.27.3155.096.091.054.010.069.0125.055.256.078.025.053.085.0240.0156.02.0
PlayerAge90sTklTklWDef 3rdMid 3rdAtt 3rdTkl.1AttTkl%LostBlocksShPassIntTkl+IntClrErr
8Netherlands26.10007.30182.000114.00091.00076.00015.00071.000149.00047.700078.00085.00015.00070.00098.000280.000147.001.000
9NetherlandsOpp27.20007.30155.00096.00091.00054.00010.00069.000125.00055.200056.00078.00025.00053.00085.000240.000156.002.000
10Norway26.80005.3087.00060.00049.00027.00011.00034.00071.00047.900037.00069.00024.00045.00050.000137.000111.001.000
11NorwayOpp26.90005.3095.00062.00050.00039.0006.00044.00082.00053.700038.00077.00018.00059.00067.000162.00086.002.000
12Sweden28.50007.30131.00078.00047.00068.00016.00050.00087.00057.500037.00076.00017.00059.00066.000197.000145.000.000
13SwedenOpp27.20007.30178.000112.00096.00067.00015.00085.000148.00057.400063.00083.00014.00069.00095.000273.000153.004.000
14USA28.80007.00117.00074.00049.00052.00016.00041.00087.00047.100046.00059.00014.00045.00079.000196.00098.003.000
15USAOpp27.00007.00179.000102.000106.00062.00011.00056.000130.00043.100074.00071.00020.00051.00086.000265.000184.001.000
16Average27.22506.15121.75073.75055.37552.12514.25042.00084.87549.562542.87567.62515.62552.00064.125185.875108.251.875
17AverageOpp26.96256.15131.37581.12572.50048.50010.37551.875105.50048.950053.62574.75018.62556.12575.750207.125135.252.250